Management of sustainment costs on expensive platforms calls for a predictive health management capability to drive logistics. One approach to cost containment is to design algorithms that are platform-neutral. These algorithms are functionally independent of knowledge related to a particular platform, thereby allowing seamless application to another platform without adding new sensors. This calls for a translation of existing control systems data into diagnostic and prognostics indicators. This also requires the development of new diagnostic and prognostic algorithms that can reason with data not originally designed to be predictive in nature.
Abnormal condition detection is an important first step in system prognosis. Abnormal conditions, also known as faults, are the first sign of a potential equipment failure at some future time. The direct cost of equipment failures is unavoidable: ultimately, the component must be replaced. However, there are indirect costs to equipment failure that are in many cases far greater than the cost of the repair. One source of indirect costs is secondary damage—e.g., component failure in the compressor stage of a gas turbine often causes damage to the rear stages. Another indirect cost is unscheduled maintenance. Generally replacement of a faulty component Before failure during scheduled maintenance is far less expensive than to have a component fail and have to shut the whole system down (e.g., power generation); moreover, guaranteed uptime is sometimes written into service contracts. Thus, for many systems there is considerable economic motivation to detect faults early and accurately.
Condition-based systems depend on reliable fault diagnostics to initiate the prognostic algorithms. Therefore optimization of the diagnostic capability to attain optimal prognostics becomes of greater importance. If diagnostics recognizes the start point of damage too late, the damage propagation models may lag reality and keep underestimating the damage. If prognostic algorithms are kicked off when there is no real damage, the benefit of true remaining life estimate is erased.
Developing a new fault classification system for each physical system (e.g., aircraft, rocket launcher, golf cart, etc.) developed with single or multiple sensors (temperatures, pressures, speeds, etc.) is a costly labor intensive process. Typical steps in fault detection include sensor validation, pre-processing, feature extraction, classification, and perhaps post-processing.